Data-Driven Spatiotemporal Modeling and Control of Nonlinear Distributed Parameter Systems and Their Applications (Intelligent Control and Learning Systems)

Xu, Bowen/ Lu, Xinjiang

Springer Verlag, Singapore（2026/01発売）

• 製本 Hardcover:ハードカバー版
• 言語 ENG
• 商品コード 9789819541140

Full Description

This book provides a systematic overview and classification on modeling and control of different types of Distributed Parameter Systems (DPSs), and develops new methods to tackle some of these unsolved problems, facilitating a better understanding of DPSs and providing references for practical problem-solving in the relevant fields. All these methods presented in this book are verified by specific scenarios. It is worth mentioning that in the context of disciplinary integration and artificial intelligence, the research ideas, methods and models in this book can be further integrated and developed. From application perspectives, they can also extend from traditional mechanical, electrical, and chemical fields to emerging fields of new energy, new materials, and multimodal information. Under the background of disciplinary integration and the development of artificial intelligence, the book will be beneficial to undergraduate and postgraduate students in interdisciplinary disciplines including manufacturing engineering, mechanical engineering, electrical engineering, computer engineering, and control engineering, etc. It is also intended for researchers and practical users in the fields of nonlinear dynamics, spatiotemporal modeling and intelligent control.

Contents

Introduction.- Spatiotemporal LS-SVM Modeling Approach for Nonlinear DPSs.- Multi-kernel Spatiotemporal Model for Large Operation Range.- Graphic Relation-based Spatiotemporal Model for Large Spatial Region.- Online Low-order Spatiotemporal LS-SVM Modeling Approach.- Online Spatiotemporal ELM Modeling Method.- Knowledge-based Spatiotemporal Fuzzy Modeling Approach.- Spatiotemporal Recurrent Neural Network Modeling Approach.- Convolution and Memory Network-based Spatiotemporal Model.- Data-driven Spatiotemporal Inverse Control Approach.- KPCA-based Spatiotemporal Model Predictive Control Approach.- Two-Layer Rules-based Spatiotemporal Fuzzy Control Strategy.- Conclusion and Prospects.